OpenRaiser/Intern-Atlas huggingface dataset:
VPTR: Efficient Transformers for Video Prediction
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, we propose a new Transformer block for video future frames prediction based on an efficient local spatial-temporal separation attention mechanism. Based on this new Transformer block, a fully autoregressive video future frames prediction Transformer is proposed. In addition, a non-autoregressive video prediction Transformer is also proposed to increase the inference speed and reduce the accumulated inference errors of its autoregressive counterpart. In order to avoid the prediction of very similar future frames, a contrastive feature loss is applied to maximize the mutual information between predicted and ground-truth future frame features. This work is the first that makes a formal comparison of the two types of attention-based video future frames prediction models over different scenarios. The proposed models reach a performance competitive with more complex state-of-the-art models. The source code is available at \emph{https://github.com/XiYe20/VPTR}.
NNLander-VeriF: A Neural Network Formal Verification Framework for Vision-Based Autonomous Aircraft Landing
Venue: arXiv cs.LG 2022
Year: 2022

In this paper, we consider the problem of formally verifying a Neural Network (NN) based autonomous landing system. In such a system, a NN controller processes images from a camera to guide the aircraft while approaching the runway. A central challenge for the safety and liveness verification of vision-based closed-loop systems is the lack of mathematical models that captures the relation between the system states (e.g., position of the aircraft) and the images processed by the vision-based NN controller. Another challenge is the limited abilities of state-of-the-art NN model checkers. Such model checkers can reason only about simple input-output robustness properties of neural networks. This limitation creates a gap between the NN model checker abilities and the need to verify a closed-loop system while considering the aircraft dynamics, the perception components, and the NN controller. To this end, this paper presents NNLander-VeriF, a framework to verify vision-based NN controllers used for autonomous landing. NNLander-VeriF addresses the challenges above by exploiting geometric models of perspective cameras to obtain a mathematical model that captures the relation between the aircraft states and the inputs to the NN controller. By converting this model into a NN (with manually assigned weights) and composing it with the NN controller, one can capture the relation between aircraft states and control actions using one augmented NN. Such an augmented NN model leads to a natural encoding of the closed-loop verification into several NN robustness queries, which state-of-the-art NN model checkers can handle. Finally, we evaluate our framework to formally verify the properties of a trained NN and we show its efficiency.
Measurement of software development effort estimation bias: Avoiding biased measures of estimation bias
Venue: arXiv cs.SE 2022
Year: 2022

In this paper, we propose improvements in how estimation bias, e.g., the tendency towards under-estimating the effort, is measured. The proposed approach emphasizes the need to know what the estimates are meant to represent, i.e., the type of estimate we evaluate and the need for a match between the type of estimate given and the bias measure used. We show that even perfect estimates of the mean effort will not lead to an expectation of zero estimation bias when applying the frequently used bias measure: (actual effort - estimated effort)/actual effort. This measure will instead reward under-estimates of the mean effort. We also provide examples of bias measures that match estimates of the mean and the median effort, and argue that there are, in general, no practical bias measures for estimates of the most likely effort. The paper concludes with implications for the evaluation of bias of software development effort estimates.
On Triangulation as a Form of Self-Supervision for 3D Human Pose Estimation
Venue: arXiv cs.CV 2022
Year: 2022

Supervised approaches to 3D pose estimation from single images are remarkably effective when labeled data is abundant. However, as the acquisition of ground-truth 3D labels is labor intensive and time consuming, recent attention has shifted towards semi- and weakly-supervised learning. Generating an effective form of supervision with little annotations still poses major challenge in crowded scenes. In this paper we propose to impose multi-view geometrical constraints by means of a weighted differentiable triangulation and use it as a form of self-supervision when no labels are available. We therefore train a 2D pose estimator in such a way that its predictions correspond to the re-projection of the triangulated 3D pose and train an auxiliary network on them to produce the final 3D poses. We complement the triangulation with a weighting mechanism that alleviates the impact of noisy predictions caused by self-occlusion or occlusion from other subjects. We demonstrate the effectiveness of our semi-supervised approach on Human3.6M and MPI-INF-3DHP datasets, as well as on a new multi-view multi-person dataset that features occlusion.
Indoor SLAM Using a Foot-mounted IMU and the local Magnetic Field
Venue: arXiv cs.RO 2022
Year: 2022

In this paper, a simultaneous localization and mapping (SLAM) algorithm for tracking the motion of a pedestrian with a foot-mounted inertial measurement unit (IMU) is proposed. The algorithm uses two maps, namely, a motion map and a magnetic field map. The motion map captures typical motion patterns of pedestrians in buildings that are constrained by e.g. corridors and doors. The magnetic map models local magnetic field anomalies in the environment using a Gaussian process (GP) model and uses them as position information. These maps are used in a Rao-Blackwellized particle filter (RBPF) to correct the pedestrian position and orientation estimates from the pedestrian dead-reckoning (PDR). The PDR is computed using an extended Kalman filter with zero-velocity updates (ZUPT-EKF). The algorithm is validated using real experimental sequences and the results show the efficacy of the algorithm in localizing pedestrians in indoor environments.
Radial Autoencoders for Enhanced Anomaly Detection
Venue: arXiv cs.LG 2022
Year: 2022

In classification problems, supervised machine-learning methods outperform traditional algorithms, thanks to the ability of neural networks to learn complex patterns. However, in two-class classification tasks like anomaly or fraud detection, unsupervised methods could do even better, because their prediction is not limited to previously learned types of anomalies. An intuitive approach of anomaly detection can be based on the distances from the centers of mass of the two respective classes. Autoencoders, although trained without supervision, can also detect anomalies: considering the center of mass of the normal points, reconstructions have now radii, with largest radii most likely indicating anomalous points. Of course, radii-based classification were already possible without interposing an autoencoder. In any space, radial classification can be operated, to some extent. In order to outperform it, we proceed to radial deformations of data (i.e. centric compression or expansions of axes) and autoencoder training. Any autoencoder that makes use of a data center is here baptized a centric autoencoder (cAE). A special type is the cAE trained with a uniformly compressed dataset, named the centripetal autoencoder (cpAE). The new concept is studied here in relation with a schematic artificial dataset, and the derived methods show consistent score improvements. But tested on real banking data, our radial deformation supervised algorithms alone still perform better that cAEs, as expected from most supervised methods; nonetheless, in hybrid approaches, cAEs can be combined with a radial deformation of space, improving its classification score. We expect that centric autoencoders will become irreplaceable objects in anomaly live detection based on geometry, thanks to their ability to stem naturally on geometrical algorithms and to their native capability of detecting unknown anomaly types.
Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design
Venue: arXiv cs.LG 2022
Year: 2022

Being able to predict the performance of circuits without running expensive simulations is a desired capability that can catalyze automated design. In this paper, we present a supervised pretraining approach to learn circuit representations that can be adapted to new circuit topologies or unseen prediction tasks. We hypothesize that if we train a neural network (NN) that can predict the output DC voltages of a wide range of circuit instances it will be forced to learn generalizable knowledge about the role of each circuit element and how they interact with each other. The dataset for this supervised learning objective can be easily collected at scale since the required DC simulation to get ground truth labels is relatively cheap. This representation would then be helpful for few-shot generalization to unseen circuit metrics that require more time consuming simulations for obtaining the ground-truth labels. To cope with the variable topological structure of different circuits we describe each circuit as a graph and use graph neural networks (GNNs) to learn node embeddings. We show that pretraining GNNs on prediction of output node voltages can encourage learning representations that can be adapted to new unseen topologies or prediction of new circuit level properties with up to 10x more sample efficiency compared to a randomly initialized model. We further show that we can improve sample efficiency of prior SoTA model-based optimization methods by 2x (almost as good as using an oracle model) via fintuning pretrained GNNs as the feature extractor of the learned models.
Self-Supervised Leaf Segmentation under Complex Lighting Conditions
Venue: arXiv cs.CV 2022
Year: 2022

As an essential prerequisite task in image-based plant phenotyping, leaf segmentation has garnered increasing attention in recent years. While self-supervised learning is emerging as an effective alternative to various computer vision tasks, its adaptation for image-based plant phenotyping remains rather unexplored. In this work, we present a self-supervised leaf segmentation framework consisting of a self-supervised semantic segmentation model, a color-based leaf segmentation algorithm, and a self-supervised color correction model. The self-supervised semantic segmentation model groups the semantically similar pixels by iteratively referring to the self-contained information, allowing the pixels of the same semantic object to be jointly considered by the color-based leaf segmentation algorithm for identifying the leaf regions. Additionally, we propose to use a self-supervised color correction model for images taken under complex illumination conditions. Experimental results on datasets of different plant species demonstrate the potential of the proposed self-supervised framework in achieving effective and generalizable leaf segmentation.
Empirical Standards for Repository Mining
Venue: arXiv cs.DL 2022
Year: 2022

The purpose of scholarly peer review is to evaluate the quality of scientific manuscripts. However, study after study demonstrates that peer review neither effectively nor reliably assesses research quality. Empirical standards attempt to address this problem by modelling a scientific community's expectations for each kind of empirical study conducted in that community. This should enhance not only the quality of research but also the reliability and predictability of peer review, as scientists adopt the standards in both their researcher and reviewer roles. However, these improvements depend on the quality and adoption of the standards. This tutorial will therefore present the empirical standard for mining software repositories, both to communicate its contents and to get feedback from the attendees. The tutorial will be organized into three parts: (1) brief overview of the empirical standards project; (2) detailed presentation of the repository mining standard; (3) discussion and suggestions for improvement.
pycefr: Python Competency Level through Code Analysis
Venue: arXiv cs.SE 2022
Year: 2022

Python is known to be a versatile language, well suited both for beginners and advanced users. Some elements of the language are easier to understand than others: some are found in any kind of code, while some others are used only by experienced programmers. The use of these elements lead to different ways to code, depending on the experience with the language and the knowledge of its elements, the general programming competence and programming skills, etc. In this paper, we present pycefr, a tool that detects the use of the different elements of the Python language, effectively measuring the level of Python proficiency required to comprehend and deal with a fragment of Python code. Following the well-known Common European Framework of Reference for Languages (CEFR), widely used for natural languages, pycefr categorizes Python code in six levels, depending on the proficiency required to create and understand it. We also discuss different use cases for pycefr: identifying code snippets that can be understood by developers with a certain proficiency, labeling code examples in online resources such as Stackoverflow and GitHub to suit them to a certain level of competency, helping in the onboarding process of new developers in Open Source Software projects, etc. A video shows availability and usage of the tool: https://tinyurl.com/ypdt3fwe.
The Sound of Bounding-Boxes
Venue: arXiv cs.CV 2022
Year: 2022

In the task of audio-visual sound source separation, which leverages visual information for sound source separation, identifying objects in an image is a crucial step prior to separating the sound source. However, existing methods that assign sound on detected bounding boxes suffer from a problem that their approach heavily relies on pre-trained object detectors. Specifically, when using these existing methods, it is required to predetermine all the possible categories of objects that can produce sound and use an object detector applicable to all such categories. To tackle this problem, we propose a fully unsupervised method that learns to detect objects in an image and separate sound source simultaneously. As our method does not rely on any pre-trained detector, our method is applicable to arbitrary categories without any additional annotation. Furthermore, although being fully unsupervised, we found that our method performs comparably in separation accuracy.
Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data
Venue: arXiv cs.LG 2022
Year: 2022

Applying machine learning (ML) in design flow is a popular trend in EDA with various applications from design quality predictions to optimizations. Despite its promise, which has been demonstrated in both academic researches and industrial tools, its effectiveness largely hinges on the availability of a large amount of high-quality training data. In reality, EDA developers have very limited access to the latest design data, which is owned by design companies and mostly confidential. Although one can commission ML model training to a design company, the data of a single company might be still inadequate or biased, especially for small companies. Such data availability problem is becoming the limiting constraint on future growth of ML for chip design. In this work, we propose an Federated-Learning based approach for well-studied ML applications in EDA. Our approach allows an ML model to be collaboratively trained with data from multiple clients but without explicit access to the data for respecting their data privacy. To further strengthen the results, we co-design a customized ML model FLNet and its personalization under the decentralized training scenario. Experiments on a comprehensive dataset show that collaborative training improves accuracy by 11% compared with individual local models, and our customized model FLNet significantly outperforms the best of previous routability estimators in this collaborative training flow.
ITTR: Unpaired Image-to-Image Translation with Transformers
Venue: arXiv cs.CV 2022
Year: 2022

Unpaired image-to-image translation is to translate an image from a source domain to a target domain without paired training data. By utilizing CNN in extracting local semantics, various techniques have been developed to improve the translation performance. However, CNN-based generators lack the ability to capture long-range dependency to well exploit global semantics. Recently, Vision Transformers have been widely investigated for recognition tasks. Though appealing, it is inappropriate to simply transfer a recognition-based vision transformer to image-to-image translation due to the generation difficulty and the computation limitation. In this paper, we propose an effective and efficient architecture for unpaired Image-to-Image Translation with Transformers (ITTR). It has two main designs: 1) hybrid perception block (HPB) for token mixing from different receptive fields to utilize global semantics; 2) dual pruned self-attention (DPSA) to sharply reduce the computational complexity. Our ITTR outperforms the state-of-the-arts for unpaired image-to-image translation on six benchmark datasets.
ReplaceBlock: An improved regularization method based on background information
Venue: arXiv cs.CV 2022
Year: 2022

Attention mechanism, being frequently used to train networks for better feature representations, can effectively disentangle the target object from irrelevant objects in the background. Given an arbitrary image, we find that the background's irrelevant objects are most likely to occlude/block the target object. We propose, based on this finding, a ReplaceBlock to simulate the situations when the target object is partially occluded by the objects that are deemed as background. Specifically, ReplaceBlock erases the target object in the image, and then generates a feature map with only irrelevant objects and background by the model. Finally, some regions in the background feature map are used to replace some regions of the target object in the original image feature map. In this way, ReplaceBlock can effectively simulate the feature map of the occluded image. The experimental results show that ReplaceBlock works better than DropBlock in regularizing convolutional networks.
Self-supervised 360$^{\circ}$ Room Layout Estimation
Venue: arXiv cs.CV 2022
Year: 2022

We present the first self-supervised method to train panoramic room layout estimation models without any labeled data. Unlike per-pixel dense depth that provides abundant correspondence constraints, layout representation is sparse and topological, hindering the use of self-supervised reprojection consistency on images. To address this issue, we propose Differentiable Layout View Rendering, which can warp a source image to the target camera pose given the estimated layout from the target image. As each rendered pixel is differentiable with respect to the estimated layout, we can now train the layout estimation model by minimizing reprojection loss. Besides, we introduce regularization losses to encourage Manhattan alignment, ceiling-floor alignment, cycle consistency, and layout stretch consistency, which further improve our predictions. Finally, we present the first self-supervised results on ZilloIndoor and MatterportLayout datasets. Our approach also shows promising solutions in data-scarce scenarios and active learning, which would have an immediate value in the real estate virtual tour software. Code is available at https://github.com/joshua049/Stereo-360-Layout.
Combination of Time-domain, Frequency-domain, and Cepstral-domain Acoustic Features for Speech Commands Classification
Venue: arXiv cs.SD 2022
Year: 2022

In speech-related classification tasks, frequency-domain acoustic features such as logarithmic Mel-filter bank coefficients (FBANK) and cepstral-domain acoustic features such as Mel-frequency cepstral coefficients (MFCC) are often used. However, time-domain features perform more effectively in some sound classification tasks which contain non-vocal or weakly speech-related sounds. We previously proposed a feature called bit sequence representation (BSR), which is a time-domain binary acoustic feature based on the raw waveform. Compared with MFCC, BSR performed better in environmental sound detection and showed comparable accuracy performance in limited-vocabulary speech recognition tasks. In this paper, we propose a novel improvement BSR feature called BSR-float16 to represent floating-point values more precisely. We experimentally demonstrated the complementarity among time-domain, frequency-domain, and cepstral-domain features using a dataset called Speech Commands proposed by Google. Therefore, we used a simple back-end score fusion method to improve the final classification accuracy. The fusion results also showed better noise robustness.
An Overview of Indian Language Datasets used for Text Summarization
Venue: arXiv cs.CL 2022
Year: 2022

In this paper, we survey Text Summarization (TS) datasets in Indian Languages (ILs), which are also low-resource languages (LRLs). We seek to answer one primary question: is the pool of Indian Language Text Summarization (ILTS) dataset growing or is there a resource poverty? To an-swer the primary question, we pose two sub-questions that we seek about ILTS datasets: first, what characteristics: format and domain do ILTS datasets have? Second, how different are those characteristics of ILTS datasets from high-resource languages (HRLs) particularly English. We focus on datasets reported in published ILTS research works during 2012-2022. The survey of ILTS and English datasets reveals two similarities and one contrast. The two similarities are: first, the domain of dataset commonly is news (Hermann et al., 2015). The second similarity is the format of the dataset which is both extractive and abstractive. The contrast is in how the research in dataset development has progressed. ILs face a slow speed of development and public release of datasets as compared with English. We argue that the relatively lower number of ILTS datasets is because of two reasons: first, absence of a dedicated forum for developing TS tools and resources; and second, lack of shareable standard datasets in the public domain.
Disparities in greenspace access during COVID-19 mobility restrictions
Venue: arXiv cs.SI 2022
Year: 2022

More than half of the human population lives in cities meaning that most people predominantly experience nature in urban greenspace. Nature exposure is an important contributor to social, mental and physical health. As the world faces a pandemic which threatens the physical and mental health of billions of people, it is crucial to understand that all have the possibility to access nature exposure to alleviate some of these challenges. Here, for the first time, we integrate data from Facebook, Twitter, and Google Search users to show that people looked for greenspace during COVID-19 mobility restrictions but may not have always managed to reach it. People spent more time in areas with greenspace when they could and that depended on the level of multiple deprivation in the neighbourhood where the greenspace was embedded. Importantly, while people sought greenspace throughout the first 20 months of the pandemic, this preference intensified through the waves of lockdown. Living in an affluent area conferred a greenspace advantage in London and Paris but we find that in Berlin more deprived neighbourhoods sought greenspace more, including outside their neighbourhood. This highlights the need to understand how greenspace access and deprivation interact to create more sustainable communities.